Why be FAIR?

Whilst making data FAIR has obvious benefits, translating the FAIR principles into practice is challenging. The resources (people/time/money) needed are not trivial. So why invest the time and effort in to being FAIR?

BENEFITS

Making research data FAIR provides a range of benefits to researchers, research communities, research infrastructure facilities and research organisations, including (www.ardc.edu.au):

  • achieving maximum impact from research
  • appreciating and recognising the value of the data – allows us to see data as an important asset
  • enabling new scientific discoveries
  • increasing uptake of the research
  • creating a standardized set of tools for data storage, retrieval, and reporting
  • creating opportunities for funding and collaboration
  • improving efficiencies
  • increases the visibility and citations of research
  • creating recognizable formats thereby making data more meaningful and usable
  • staying aligned with international standards and approaches
  • adherence to increasing publication requirements to provide FAIR data

CONSEQUENCES

If data are not FAIR there can be consequences to researchers, research communities, research infrastructure facilities and research organisations, including:

  • reducing scientific impact
  • increasing project costs
  • increasing duplication of effort
  • failing to meet international standards and approaches
  • staying relevant while future proofing
  • inability to answer important research questions
  • missed opportunities/collaborations

CHALLENGES

The greatest challenge in transitioning data to FAIR are the availability of (or lack of) resources and infrastructure that allow for systematic change. Agreed standards and vocabularies must occur at every stage of the (meta)data pipeline, including acquisition, post-processing, annotation, analysis, storage, and publication processes. Moreover, these ‘procedures and protocols’ should also align with global standards. Naturally, adoption of FAIR data principals will take time and require solutions at incremental scales. Some challenges include:

  • data volumes and data storage
  • databases
  • unique identifiers
  • reliability of ancillary data
  • licensing and IP
  • upload/download speeds
  • standardisation (e.g., metadata, vocabularies, data collection, and storage)
  • maintenance and management
  • support and education
  • staying up to date, e.g., with changing technologies- future proofing
  • encouraging individuals/teams/projects to abide by the FAIR principles